There is a growing demand for the health condition of patients to be monitored over a long duration. This is true especially for patients with chronic diseases such as asthma. Auscultation using a stethoscope has been the most popular medical tool used for this purpose, but the design of the stethoscope does not allow the monitoring to be made in the absence of a physician. This poses a restriction on the location and duration of the monitoring.
Rapid technological advances in recent years have led to the use of electronic systems for automatic respiration measurement. However, such systems require relatively special equipment and/or bulky equipment that can only be provided in the hospitals. The systems are designed with bundles of wires connecting the equipments to sensors deployed on the human body. These systems attach sensors to the skin of the patients, thereby requiring skin contact of the sensors. All these restrict the movements of the patients, are uncomfortable for the patients and are unsuitable for daily continuous monitoring purposes.
In addition, the computer aided signal detection for wheezes and snores has been investigated for several decades. Many methods have been proposed for this purpose, for example Mel-Frequency Cepstral Coefficients (MFCC), time-frequency analysis, wavelet transform, neural network and Hidden Markov Model (HMM) are among the popular methods. However these methods involve, in general, high computational complexity and are therefore not suitable for implementation for low power and long duration monitoring devices.